arXiv AI
Jul 3
What Types of Human-AI Teams Exist?
arXiv:2607. 02198v1 Announce Type: cross Abstract: Human-AI teaming has received increasing attention in the literature.
Human-AI Collaboration: From Paradoxes to Patterns
The paper demonstrates that humans and AI systems achieve better performance when collaborating rather than working alone. It investigates how two design dimensions—autonomy and initiative—shape collaboration patterns, using a paradox perspective to uncover internal tensions and map underlying paradoxes. From this analysis, the authors derive four distinct human‑AI collaboration patterns: Instruction, Delegation, Assistance, and Co‑creation.
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arXiv AI
Jul 14
Comparing Socio-technical Design Principles with Guidelines for Human-centered AI
arXiv:2607. 10331v1 Announce Type: new Abstract: Human-centered AI (HCAI) refers to guidelines or principles that aim on ethi-cally oriented design of systems.
arXiv AI
Sep 10
An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling
arXiv:2609.05552v1 Announce Type: cross Abstract: Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perc...
arXiv AI
Jul 24
The Boundaries of Automation: A Theory of Persistent Human Participation
arXiv:2607. 21547v1 Announce Type: new Abstract: The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible.
arXiv AI
Jun 6
Where's the Structure? A Systematic Literature Review of Empirical Research on Human-AI Collaboration and Hybrid Intelligence for Learning
arXiv:2606. 05222v1 Announce Type: cross Abstract: Artificial intelligence (AI) has been applied across educational contexts to support learning.
arXiv AI
Aug 25
A Survey on Human-AI Collaboration with Large Foundation Models
The paper surveys how Large Foundation Models (LFMs) can be integrated into Human‑AI Collaboration (HAI) to enhance problem‑solving and decision‑making. It outlines four key areas—human‑guided model development, collaborative design principles, ethical and governance frameworks, and high‑stakes applications—while emphasizing that effective HAI systems arise from careful, human‑centered design rather than merely stronger models. The survey also identifies open challenges related to safety, fairness, and control, aiming to guide future research toward reliable, trustworthy, and beneficial LFM‑based partnerships.
arXiv AI
6d ago
Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work
The paper reports an in‑situ qualitative study of a persistent, proactive AI teammate deployed across multiple teams in a large technology company. It finds that the human‑agent workplace is in flux, with breakdowns and negotiations emerging around tacit workflow rules, the relational boundaries of the non‑human actor, and the redistribution of trust and human agency. These micro‑negotiations are used to propose a new research, design, and organizational agenda that seeks to preserve human agency when sharing workspaces with non‑human actors.
arXiv AI
Jun 18
Searching for Synergy in Shared Workspace Human-AI Collaboration
arXiv:2606. 18413v1 Announce Type: new Abstract: Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise.
arXiv AI
Jul 23
A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace
arXiv:2607. 19941v1 Announce Type: cross Abstract: As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption.
arXiv AI
3d ago
Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development
The paper examines how a large embedded systems company is transitioning to an AI‑first organization, focusing on the role of autonomous AI agents in software engineering. Through a mixed‑method study involving 40 workshop participants—scrum masters, architects, managers, and product owners—the authors identify expected impacts on team structure, required competencies, organizational strategies, and developer roles. The study concludes with a concrete roadmap and discusses implications for federated AI team formation, human‑in‑the‑loop practices, and sustainable AI adoption in embedded software engineering.
arXiv AI
Jun 10
Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI
arXiv:2606. 09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through interfaces-that supports usability, trust, and safety.
arXiv AI
Aug 14
Humans are Missing from AI Coding Agent Research
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.